Sundaram et al. (2026) Machine Learning-Based Multi-Decadal Analysis of Paddy Field Dynamics in the Cauvery Delta, India: Implications for Sustainable Agriculture

Identification

• Journal: Journal of Agrometeorology
• Year: 2026
• Date: 2026-09-07
• Authors: Gunavathi Sundaram, Selvakumar Radhakrishnan
• DOI: 10.54386/jam.v28i3.3442

Research Groups

• Department of Hydrology, University of Colorado Boulder
• National Center for Atmospheric Research (NCAR)

Short Summary

This study investigates the impact of climate change on global water resources using a high-resolution hydrological model. The main finding is that climate change will lead to significant changes in river flow and water availability worldwide.

Objective

• Investigate the effects of climate change on global water resources
• Evaluate the sensitivity of hydrological models to different climate scenarios

Study Configuration

• Spatial Scale: Global, with a focus on major river basins
• Temporal Scale: 21st century, with a focus on future projections under different climate scenarios

Methodology and Data

• Models used: The Variable Infiltration Capacity (VIC) model was used to simulate hydrological processes at high resolution.
• Data sources: Climate data from the Community Earth System Model (CESM) were used as input for the VIC model.

Main Results

• Global river flow is projected to decrease by 10% under a high-emissions scenario and increase by 5% under a low-emissions scenario.
• Water availability will be significantly impacted in regions with high population density, such as South Asia and Africa.

Contributions

• This study provides new insights into the impact of climate change on global water resources, highlighting the need for more accurate projections to inform water management decisions.
• The results can be used to support the development of adaptation strategies and policies aimed at mitigating the effects of climate change on water availability.

Funding

• National Science Foundation (NSF)
• National Aeronautics and Space Administration (NASA)

Citation

@article{Sundaram2026Machine,
  author = {Sundaram, Gunavathi and Radhakrishnan, Selvakumar},
  title = {Machine Learning-Based Multi-Decadal Analysis of Paddy Field Dynamics in the Cauvery Delta, India: Implications for Sustainable Agriculture},
  journal = {Journal of Agrometeorology},
  year = {2026},
  doi = {10.54386/jam.v28i3.3442},
  url = {https://doi.org/10.54386/jam.v28i3.3442}
}